Abstract
In this study we investigate the potential of applying the radial basis function (RBF) neural network architecture for the classification of patients with chronic renal failure (CRF) through quantitative parameters derived from EEG. To provide an objective EEG assessment of cerebral disturbances in CRF, we set up and tested a procedure of classification based on artificial neural networks (ANN) using RBF trained with quantitative parameters derived from EEG. A set sample was prepared based on EEG of 17 patients and 18 age-matched control subjects. Quantitative EEG (qEEG) found significant differences between groups. Accuracy of ANN-based classification in this set was 86.6%. Our results indicate that a classification system based on RBF neural networks may help in the automation of EEG analysis for diagnosis and prospective clinical evaluation of CRF patients.
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References
Gabor, A.J., Seyal, M.: Automated interictal EEG spike detection using artificial neural networks. Electroencephalography and Clinical Neurophysiology 83(5), 271–280 (1992), ISSN: 0013-4694
Gaztelu, J.M., Martino, M.E., Fernández-Lorente, J., Romero-Vives, M., de Vicente, E., Bárcena, R.: Sleep EEG spectral characteristics disclose subclinical hepatic encephalopathy. In: Society for Neuroscience XXXI Annual Meeting. San Diego, California, November 10-15 (2001)
Martino, M.E.: Análisis cuantitativo del electroencefalograma en pacientes cirróticos. Implicaciones diagnósticas y terapéuticas. PhD Doctoral Thesis, UAM, Madrid (2003)
Barios, J.: Caracterización de la actividad oscilatoria cerebral durante el sueño lento. Alteraciones en la encefalopata hepática mínima y modicaciones con el trasplante hepático. PhD Doctoral Thesis, UAM, Madrid (2008)
Bourne, J.R., Ward, J.W., Teschan, P.E., Musso, M., Johnston, J.R., Ginn, H.E.: Quantitative assessment of the electroencephalogram in renal disease. Electroencephalography and Clinical Neurophysiology 39, 377–388 (1975)
Balzar, E., Saletu, B., Khoss, A.: Quantitative EEG: investigation in children with end stage renal disease before and after haemodialysis. Clinical EEG (electroencephalography) 17, 195–202 (1986)
Röhl, J., Harms, L., Pommer, W.: Quantitative EEG findings in patients with chronic renal failure. European Journal of Medical Research 12, 173–178 (2007)
Pellegrini, A., Ubiali, E., Orsato, R., Schiff, S., Gatta, A., Castellaro, A., Casagrande, A., Amodio, P.: Electroencephalographic staging of hepatic encephalopathy by an artificial neural network and an expert system. Neurophysiologie Clinique = Clinical Neurophysiology 35, 162–167 (2005)
Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology. Heart and Circulatory Physiology 278, H2039–H2049 (2000)
R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2008), http://www.R-project.org , ISBN 3-900051-07-0
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Barios, J.A. et al. (2011). Radial Basis Function Neural Network for Classification of Quantitative EEG in Patients with Advanced Chronic Renal Failure. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) Foundations on Natural and Artificial Computation. IWINAC 2011. Lecture Notes in Computer Science, vol 6686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21344-1_43
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DOI: https://doi.org/10.1007/978-3-642-21344-1_43
Publisher Name: Springer, Berlin, Heidelberg
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